


'''simple docstring'''



from typing import Any, Dict, List, Union

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, ChunkPipeline


if is_vision_available():
      from PIL import Image

      from ..image_utils import load_image

if is_torch_available():
      import torch

      from transformers.modeling_outputs import BaseModelOutput

      from ..models.auto.modeling_auto import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING

A__						:   Optional[int]				=    logging.get_logger(__name__)





@add_end_docstrings(_UpperCAmelCase    )
class   UpperCAmelCase_    (_UpperCAmelCase    ):





    """simple docstring"""






    def __init__(				self     ,	**SCREAMING_SNAKE_CASE_      )							->	List[str]:
         super().__init__(**SCREAMING_SNAKE_CASE_      )

         if self.framework == "tf":
              raise ValueError(f'The {self.__class__} is only available in PyTorch.'      )

         requires_backends(self     ,	'vision'      )
         self.check_model_type(SCREAMING_SNAKE_CASE_      )







    def __call__(				self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_ = None     ,	**SCREAMING_SNAKE_CASE_     ,	)							->	Optional[Any]:
         if "text_queries" in kwargs:
              __lowerCamelCase  :      List[Any]														=						kwargs.pop('text_queries'      )

         if isinstance(SCREAMING_SNAKE_CASE_     ,	(str, Image.Image)      ):
              __lowerCamelCase  :      str														=						{'image': image, 'candidate_labels': candidate_labels}
         else:
              __lowerCamelCase  :      List[str]														=						image
         __lowerCamelCase  :      Optional[int]														=						super().__call__(SCREAMING_SNAKE_CASE_     ,	**SCREAMING_SNAKE_CASE_      )
         return results







    def  lowercase_							(				self     ,	**SCREAMING_SNAKE_CASE_      )							->	List[Any]:
         __lowerCamelCase  :      Union[str, Any]														=						{}
         if "threshold" in kwargs:
              __lowerCamelCase  :      Any														=						kwargs['threshold']
         if "top_k" in kwargs:
              __lowerCamelCase  :      Tuple														=						kwargs['top_k']
         return {}, {}, postprocess_params







    def  lowercase_							(				self     ,	SCREAMING_SNAKE_CASE_      )							->	List[str]:
         __lowerCamelCase  :      Union[str, Any]														=						load_image(inputs['image']      )
         __lowerCamelCase  :      int														=						inputs['candidate_labels']
         if isinstance(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      ):
              __lowerCamelCase  :      Optional[int]														=						candidate_labels.split(','      )

         __lowerCamelCase  :      int														=						torch.tensor([[image.height, image.width]]     ,	dtype=torch.intaa      )
         for i, candidate_label in enumerate(SCREAMING_SNAKE_CASE_      ):
              __lowerCamelCase  :      Optional[int]														=						self.tokenizer(SCREAMING_SNAKE_CASE_     ,	return_tensors=self.framework      )
              __lowerCamelCase  :      str														=						self.image_processor(SCREAMING_SNAKE_CASE_     ,	return_tensors=self.framework      )
              yield {
                  "is_last": i == len(SCREAMING_SNAKE_CASE_      ) - 1,
                  "target_size": target_size,
                  "candidate_label": candidate_label,
                  **text_inputs,
                  **image_features,
              }







    def  lowercase_							(				self     ,	SCREAMING_SNAKE_CASE_      )							->	Any:
         __lowerCamelCase  :      Optional[Any]														=						model_inputs.pop('target_size'      )
         __lowerCamelCase  :      List[Any]														=						model_inputs.pop('candidate_label'      )
         __lowerCamelCase  :      List[Any]														=						model_inputs.pop('is_last'      )

         __lowerCamelCase  :      str														=						self.model(**SCREAMING_SNAKE_CASE_      )

         __lowerCamelCase  :      str														=						{'target_size': target_size, 'candidate_label': candidate_label, 'is_last': is_last, **outputs}
         return model_outputs







    def  lowercase_							(				self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_=0.1     ,	SCREAMING_SNAKE_CASE_=None      )							->	Tuple:
         __lowerCamelCase  :      Any														=						[]
         for model_output in model_outputs:
              __lowerCamelCase  :      Tuple														=						model_output['candidate_label']
              __lowerCamelCase  :      str														=						BaseModelOutput(SCREAMING_SNAKE_CASE_      )
              __lowerCamelCase  :      Dict														=						self.image_processor.post_process_object_detection(
                  outputs=SCREAMING_SNAKE_CASE_     ,	threshold=SCREAMING_SNAKE_CASE_     ,	target_sizes=model_output['target_size']      )[0]

              for index in outputs["scores"].nonzero():
                   __lowerCamelCase  :      Optional[int]														=						outputs['scores'][index].item()
                   __lowerCamelCase  :      List[Any]														=						self._get_bounding_box(outputs['boxes'][index][0]      )

                   __lowerCamelCase  :      Dict														=						{'score': score, 'label': label, 'box': box}
                   results.append(SCREAMING_SNAKE_CASE_      )

         __lowerCamelCase  :      str														=						sorted(SCREAMING_SNAKE_CASE_     ,	key=lambda SCREAMING_SNAKE_CASE_      : x["score"]     ,	reverse=SCREAMING_SNAKE_CASE_      )
         if top_k:
              __lowerCamelCase  :      List[str]														=						results[:top_k]

         return results







    def  lowercase_							(				self     ,	SCREAMING_SNAKE_CASE_      )							->	Dict[str, int]:
         if self.framework != "pt":
              raise ValueError('The ZeroShotObjectDetectionPipeline is only available in PyTorch.'      )
         __lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase  :      Union[str, Any]														=						box.int().tolist()
         __lowerCamelCase  :      Any														=						{
             'xmin': xmin,
             'ymin': ymin,
             'xmax': xmax,
             'ymax': ymax,
         }
         return bbox










import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartaaTokenizer, MBartaaTokenizerFast, is_torch_available
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
)

from ...test_tokenization_common import TokenizerTesterMixin


_SCREAMING_SNAKE_CASE     :					List[str]      =       get_tests_dir("fixtures/test_sentencepiece.model")

if is_torch_available():
 from transformers.models.mbart.modeling_mbart import shift_tokens_right

_SCREAMING_SNAKE_CASE     :					Any      =       25_00_04
_SCREAMING_SNAKE_CASE     :					Any      =       25_00_20

@require_sentencepiece
@require_tokenizers
class     A__   (       snake_case__			,   unittest.TestCase      ):







    """simple docstring"""

    __magic_name__         =						MBartaaTokenizer
    __magic_name__         =						MBartaaTokenizerFast
    __magic_name__         =						True
    __magic_name__         =						True
    def 						a_     (			self       ):
         super().setUp()

         # We have a SentencePiece fixture for testing
         snake_case											=							MBartaaTokenizer(__snake_case     ,   src_lang='''en_XX'''     ,   tgt_lang='''ro_RO'''     ,   keep_accents=__snake_case       )
         tokenizer.save_pretrained(self.tmpdirname       )
    def 						a_     (			self       ):
         snake_case											=							'''<s>'''
         snake_case											=							0

         self.assertEqual(self.get_tokenizer()._convert_token_to_id(__snake_case       )     ,   __snake_case       )
         self.assertEqual(self.get_tokenizer()._convert_id_to_token(__snake_case       )     ,   __snake_case       )
    def 						a_     (			self       ):
         snake_case											=							list(self.get_tokenizer().get_vocab().keys()       )

         self.assertEqual(vocab_keys[0]     ,   '''<s>'''       )
         self.assertEqual(vocab_keys[1]     ,   '''<pad>'''       )
         self.assertEqual(vocab_keys[-1]     ,   '''<mask>'''       )
         self.assertEqual(len(__snake_case       )     ,   1_0_5_4       )
    def 						a_     (			self       ):
         self.assertEqual(self.get_tokenizer().vocab_size     ,   1_0_5_4       )
    def 						a_     (			self       ):
         snake_case											=							MBartaaTokenizer(__snake_case     ,   src_lang='''en_XX'''     ,   tgt_lang='''ro_RO'''     ,   keep_accents=__snake_case       )

         snake_case											=							tokenizer.tokenize('''This is a test'''       )
         self.assertListEqual(__snake_case     ,   ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']       )

         self.assertListEqual(
             tokenizer.convert_tokens_to_ids(__snake_case       )     ,   [value + tokenizer.fairseq_offset for value in [2_8_5, 4_6, 1_0, 1_7_0, 3_8_2]]     ,   )

         snake_case											=							tokenizer.tokenize('''I was born in 92000, and this is falsé.'''       )
         self.assertListEqual(
             __snake_case     ,   [SPIECE_UNDERLINE + '''I''', SPIECE_UNDERLINE + '''was''', SPIECE_UNDERLINE + '''b''', '''or''', '''n''', SPIECE_UNDERLINE + '''in''', SPIECE_UNDERLINE + '''''', '''9''', '''2''', '''0''', '''0''', '''0''', ''',''', SPIECE_UNDERLINE + '''and''', SPIECE_UNDERLINE + '''this''', SPIECE_UNDERLINE + '''is''', SPIECE_UNDERLINE + '''f''', '''al''', '''s''', '''é''', '''.''']     ,   )
         snake_case											=							tokenizer.convert_tokens_to_ids(__snake_case       )
         self.assertListEqual(
             __snake_case     ,   [
                 value + tokenizer.fairseq_offset
                 for value in [8, 2_1, 8_4, 5_5, 2_4, 1_9, 7, 2, 6_0_2, 3_4_7, 3_4_7, 3_4_7, 3, 1_2, 6_6, 4_6, 7_2, 8_0, 6, 2, 4]
             ]     ,   )

         snake_case											=							tokenizer.convert_ids_to_tokens(__snake_case       )
         self.assertListEqual(
             __snake_case     ,   [SPIECE_UNDERLINE + '''I''', SPIECE_UNDERLINE + '''was''', SPIECE_UNDERLINE + '''b''', '''or''', '''n''', SPIECE_UNDERLINE + '''in''', SPIECE_UNDERLINE + '''''', '''<unk>''', '''2''', '''0''', '''0''', '''0''', ''',''', SPIECE_UNDERLINE + '''and''', SPIECE_UNDERLINE + '''this''', SPIECE_UNDERLINE + '''is''', SPIECE_UNDERLINE + '''f''', '''al''', '''s''', '''<unk>''', '''.''']     ,   )
    @slow
    def 						a_     (			self       ):
         # fmt: off
         snake_case											=							{'''input_ids''': [[2_5_0_0_0_4, 1_1_0_6_2, 8_2_7_7_2, 7, 1_5, 8_2_7_7_2, 5_3_8, 5_1_5_2_9, 2_3_7, 1_7_1_9_8, 1_2_9_0, 2_0_6, 9, 2_1_5_1_7_5, 1_3_1_4, 1_3_6, 1_7_1_9_8, 1_2_9_0, 2_0_6, 9, 5_6_3_5_9, 4_2, 1_2_2_0_0_9, 9, 1_6_4_6_6, 1_6, 8_7_3_4_4, 4_5_3_7, 9, 4_7_1_7, 7_8_3_8_1, 6, 1_5_9_9_5_8, 7, 1_5, 2_4_4_8_0, 6_1_8, 4, 5_2_7, 2_2_6_9_3, 5_4_2_8, 4, 2_7_7_7, 2_4_4_8_0, 9_8_7_4, 4, 4_3_5_2_3, 5_9_4, 4, 8_0_3, 1_8_3_9_2, 3_3_1_8_9, 1_8, 4, 4_3_5_2_3, 2_4_4_4_7, 1_2_3_9_9, 1_0_0, 2_4_9_5_5, 8_3_6_5_8, 9_6_2_6, 1_4_4_0_5_7, 1_5, 8_3_9, 2_2_3_3_5, 1_6, 1_3_6, 2_4_9_5_5, 8_3_6_5_8, 8_3_4_7_9, 1_5, 3_9_1_0_2, 7_2_4, 1_6, 6_7_8, 6_4_5, 2_7_8_9, 1_3_2_8, 4_5_8_9, 4_2, 1_2_2_0_0_9, 1_1_5_7_7_4, 2_3, 8_0_5, 1_3_2_8, 4_6_8_7_6, 7, 1_3_6, 5_3_8_9_4, 1_9_4_0, 4_2_2_2_7, 4_1_1_5_9, 1_7_7_2_1, 8_2_3, 4_2_5, 4, 2_7_5_1_2, 9_8_7_2_2, 2_0_6, 1_3_6, 5_5_3_1, 4_9_7_0, 9_1_9, 1_7_3_3_6, 5, 2], [2_5_0_0_0_4, 2_0_0_8_0, 6_1_8, 8_3, 8_2_7_7_5, 4_7, 4_7_9, 9, 1_5_1_7, 7_3, 5_3_8_9_4, 3_3_3, 8_0_5_8_1, 1_1_0_1_1_7, 1_8_8_1_1, 5_2_5_6, 1_2_9_5, 5_1, 1_5_2_5_2_6, 2_9_7, 7_9_8_6, 3_9_0, 1_2_4_4_1_6, 5_3_8, 3_5_4_3_1, 2_1_4, 9_8, 1_5_0_4_4, 2_5_7_3_7, 1_3_6, 7_1_0_8, 4_3_7_0_1, 2_3, 7_5_6, 1_3_5_3_5_5, 7, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [2_5_0_0_0_4, 5_8_1, 6_3_7_7_3, 1_1_9_4_5_5, 6, 1_4_7_7_9_7, 8_8_2_0_3, 7, 6_4_5, 7_0, 2_1, 3_2_8_5, 1_0_2_6_9, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
         # fmt: on

         self.tokenizer_integration_test_util(
             expected_encoding=__snake_case     ,   model_name='''facebook/mbart-large-50'''     ,   revision='''d3913889c59cd5c9e456b269c376325eabad57e2'''     ,   )




    def 						a_     (			self       ):
         if not self.test_slow_tokenizer:
              # as we don't have a slow version, we can't compare the outputs between slow and fast versions
              return

         snake_case											=							(self.rust_tokenizer_class, '''hf-internal-testing/tiny-random-mbart50''', {})
         for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
              with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''       ):
                   snake_case											=							self.rust_tokenizer_class.from_pretrained(__snake_case     ,   **__snake_case       )
                   snake_case											=							self.tokenizer_class.from_pretrained(__snake_case     ,   **__snake_case       )

                   snake_case											=							tempfile.mkdtemp()

                   snake_case											=							tokenizer_r.save_pretrained(__snake_case       )
                   snake_case											=							tokenizer_p.save_pretrained(__snake_case       )

                   # Checks it save with the same files + the tokenizer.json file for the fast one
                   self.assertTrue(any('''tokenizer.json''' in f for f in tokenizer_r_files       )       )
                   snake_case											=							tuple(f for f in tokenizer_r_files if '''tokenizer.json''' not in f       )
                   self.assertSequenceEqual(__snake_case     ,   __snake_case       )

                   # Checks everything loads correctly in the same way
                   snake_case											=							tokenizer_r.from_pretrained(__snake_case       )
                   snake_case											=							tokenizer_p.from_pretrained(__snake_case       )

                   # Check special tokens are set accordingly on Rust and Python
                   for key in tokenizer_pp.special_tokens_map:
                        self.assertTrue(hasattr(__snake_case     ,   __snake_case       )       )
                        # self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
                        # self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))

                   shutil.rmtree(__snake_case       )

                   # Save tokenizer rust, legacy_format=True
                   snake_case											=							tempfile.mkdtemp()

                   snake_case											=							tokenizer_r.save_pretrained(__snake_case     ,   legacy_format=__snake_case       )
                   snake_case											=							tokenizer_p.save_pretrained(__snake_case       )

                   # Checks it save with the same files
                   self.assertSequenceEqual(__snake_case     ,   __snake_case       )

                   # Checks everything loads correctly in the same way
                   snake_case											=							tokenizer_r.from_pretrained(__snake_case       )
                   snake_case											=							tokenizer_p.from_pretrained(__snake_case       )

                   # Check special tokens are set accordingly on Rust and Python
                   for key in tokenizer_pp.special_tokens_map:
                        self.assertTrue(hasattr(__snake_case     ,   __snake_case       )       )

                   shutil.rmtree(__snake_case       )

                   # Save tokenizer rust, legacy_format=False
                   snake_case											=							tempfile.mkdtemp()

                   snake_case											=							tokenizer_r.save_pretrained(__snake_case     ,   legacy_format=__snake_case       )
                   snake_case											=							tokenizer_p.save_pretrained(__snake_case       )

                   # Checks it saved the tokenizer.json file
                   self.assertTrue(any('''tokenizer.json''' in f for f in tokenizer_r_files       )       )

                   # Checks everything loads correctly in the same way
                   snake_case											=							tokenizer_r.from_pretrained(__snake_case       )
                   snake_case											=							tokenizer_p.from_pretrained(__snake_case       )

                   # Check special tokens are set accordingly on Rust and Python
                   for key in tokenizer_pp.special_tokens_map:
                        self.assertTrue(hasattr(__snake_case     ,   __snake_case       )       )

                   shutil.rmtree(__snake_case       )






@require_torch
@require_sentencepiece
@require_tokenizers
class     A__   (       unittest.TestCase      ):







    """simple docstring"""

    __magic_name__         =						'facebook/mbart-large-50-one-to-many-mmt'
    __magic_name__         =						[
        ' UN Chief Says There Is No Military Solution in Syria',
        ' Secretary-General Ban Ki-moon says his response to Russia\'s stepped up military support for Syria is that "there is no military solution" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.',
    ]
    __magic_name__         =						[
        'Şeful ONU declară că nu există o soluţie militară în Siria',
        'Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei'
        ' pentru Siria este că "nu există o soluţie militară" la conflictul de aproape cinci ani şi că noi arme nu vor'
        ' face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.',
    ]
    __magic_name__         =						[EN_CODE, 82_74, 12_78_73, 2_59_16, 7, 86_22, 20_71, 4_38, 6_74_85, 53, 18_78_95, 23, 5_17_12, 2]
    @classmethod
    def 						a_     (			cls       ):
         snake_case											=							MBartaaTokenizer.from_pretrained(
             cls.checkpoint_name     ,   src_lang='''en_XX'''     ,   tgt_lang='''ro_RO'''       )
         snake_case											=							1
         return cls
    def 						a_     (			self       ):
         self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''ar_AR''']     ,   2_5_0_0_0_1       )
         self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''en_EN''']     ,   2_5_0_0_0_4       )
         self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''ro_RO''']     ,   2_5_0_0_2_0       )
         self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''mr_IN''']     ,   2_5_0_0_3_8       )
    def 						a_     (			self       ):
         snake_case											=							self.tokenizer.batch_encode_plus(self.src_text       ).input_ids[0]
         self.assertListEqual(self.expected_src_tokens     ,   __snake_case       )
    def 						a_     (			self       ):
         self.assertIn(__snake_case     ,   self.tokenizer.all_special_ids       )
         snake_case											=							[RO_CODE, 8_8_4, 9_0_1_9, 9_6, 9, 9_1_6, 8_6_7_9_2, 3_6, 1_8_7_4_3, 1_5_5_9_6, 5, 2]
         snake_case											=							self.tokenizer.decode(__snake_case     ,   skip_special_tokens=__snake_case       )
         snake_case											=							self.tokenizer.decode(generated_ids[1:]     ,   skip_special_tokens=__snake_case       )
         self.assertEqual(__snake_case     ,   __snake_case       )
         self.assertNotIn(self.tokenizer.eos_token     ,   __snake_case       )
    def 						a_     (			self       ):
         snake_case											=							['''this is gunna be a long sentence ''' * 2_0]
         assert isinstance(src_text[0]     ,   __snake_case       )
         snake_case											=							1_0
         snake_case											=							self.tokenizer(__snake_case     ,   max_length=__snake_case     ,   truncation=__snake_case       ).input_ids[0]
         self.assertEqual(ids[0]     ,   __snake_case       )
         self.assertEqual(ids[-1]     ,   2       )
         self.assertEqual(len(__snake_case       )     ,   __snake_case       )
    def 						a_     (			self       ):
         self.assertListEqual(self.tokenizer.convert_tokens_to_ids(['''<mask>''', '''ar_AR''']       )     ,   [2_5_0_0_5_3, 2_5_0_0_0_1]       )
    def 						a_     (			self       ):
         snake_case											=							tempfile.mkdtemp()
         snake_case											=							self.tokenizer.fairseq_tokens_to_ids
         self.tokenizer.save_pretrained(__snake_case       )
         snake_case											=							MBartaaTokenizer.from_pretrained(__snake_case       )
         self.assertDictEqual(new_tok.fairseq_tokens_to_ids     ,   __snake_case       )
    @require_torch
    def 						a_     (			self       ):
         snake_case											=							self.tokenizer(self.src_text     ,   text_target=self.tgt_text     ,   padding=__snake_case     ,   return_tensors='''pt'''       )
         snake_case											=							shift_tokens_right(batch['''labels''']     ,   self.tokenizer.pad_token_id       )

         # fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
         assert batch.input_ids[1][0] == EN_CODE
         assert batch.input_ids[1][-1] == 2
         assert batch.labels[1][0] == RO_CODE
         assert batch.labels[1][-1] == 2
         assert batch.decoder_input_ids[1][:2].tolist() == [2, RO_CODE]
    @require_torch
    def 						a_     (			self       ):
         snake_case											=							self.tokenizer(
             self.src_text     ,   text_target=self.tgt_text     ,   padding=__snake_case     ,   truncation=__snake_case     ,   max_length=len(self.expected_src_tokens       )     ,   return_tensors='''pt'''     ,   )
         snake_case											=							shift_tokens_right(batch['''labels''']     ,   self.tokenizer.pad_token_id       )

         self.assertIsInstance(__snake_case     ,   __snake_case       )

         self.assertEqual((2, 1_4)     ,   batch.input_ids.shape       )
         self.assertEqual((2, 1_4)     ,   batch.attention_mask.shape       )
         snake_case											=							batch.input_ids.tolist()[0]
         self.assertListEqual(self.expected_src_tokens     ,   __snake_case       )
         self.assertEqual(2     ,   batch.decoder_input_ids[0, 0]       )  # decoder_start_token_id
         # Test that special tokens are reset
         self.assertEqual(self.tokenizer.prefix_tokens     ,   [EN_CODE]       )
         self.assertEqual(self.tokenizer.suffix_tokens     ,   [self.tokenizer.eos_token_id]       )
    def 						a_     (			self       ):
         snake_case											=							self.tokenizer(self.src_text     ,   padding=__snake_case     ,   truncation=__snake_case     ,   max_length=3     ,   return_tensors='''pt'''       )
         snake_case											=							self.tokenizer(
             text_target=self.tgt_text     ,   padding=__snake_case     ,   truncation=__snake_case     ,   max_length=1_0     ,   return_tensors='''pt'''       )
         snake_case											=							targets['''input_ids''']
         snake_case											=							shift_tokens_right(__snake_case     ,   self.tokenizer.pad_token_id       )

         self.assertEqual(batch.input_ids.shape[1]     ,   3       )
         self.assertEqual(batch.decoder_input_ids.shape[1]     ,   1_0       )




    @require_torch
    def 						a_     (			self       ):
         snake_case											=							self.tokenizer._build_translation_inputs(
             '''A test'''     ,   return_tensors='''pt'''     ,   src_lang='''en_XX'''     ,   tgt_lang='''ar_AR'''       )

         self.assertEqual(
             nested_simplify(__snake_case       )     ,   {
                 # en_XX, A, test, EOS
                 '''input_ids''': [[2_5_0_0_0_4, 6_2, 3_0_3_4, 2]],
                 '''attention_mask''': [[1, 1, 1, 1]],
                 # ar_AR
                 '''forced_bos_token_id''': 2_5_0_0_0_1,
             }     ,   )




